Noncontact Multiperson Respiratory Detection Method Based on Blind Source Separation
In recent years, there has been an increasing interest in respiratory monitoring in multiperson environments and simultaneous monitoring of the health status of multiple people. Among the algorithms developed for multiperson respiratory detection, blind source separation algorithms have attracted th...
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| Format: | Article |
| Language: | English |
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China Science Publishing & Media Ltd. (CSPM)
2025-02-01
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| Series: | Leida xuebao |
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| Online Access: | https://radars.ac.cn/cn/article/doi/10.12000/JR24115 |
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| _version_ | 1832591759496970240 |
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| author | Xuan YANG Ziying WANG Li ZHANG Heng ZHAO Hong HONG |
| author_facet | Xuan YANG Ziying WANG Li ZHANG Heng ZHAO Hong HONG |
| author_sort | Xuan YANG |
| collection | DOAJ |
| description | In recent years, there has been an increasing interest in respiratory monitoring in multiperson environments and simultaneous monitoring of the health status of multiple people. Among the algorithms developed for multiperson respiratory detection, blind source separation algorithms have attracted the attention of researchers because they do not require prior information and are less dependent on hardware performance. However, in the context of multiperson respiratory monitoring, the current blind source separation algorithm usually separates phase signals as the source signal. This article compares the distance dimension and phase signals under Frequency-modulated continuous-wave radar, calculates the approximate error associated with using the phase signal as the source signal, and verifies the separation effect through simulations. The distance dimension signal is better to use as the source signal. In addition, this article proposes a multiperson respiratory signal separation algorithm based on noncircular complex independent component analysis and analyzes the impact of different respiratory signal parameters on the separation effect. Simulation and experimental measurements show that the proposed method is suitable for detecting multiperson respiratory signals under controlled conditions and can accurately separate respiratory signals when the angle of the two targets to the radar is 9.46°. |
| format | Article |
| id | doaj-art-37f9e2d7bc62444197d584df6451f27c |
| institution | Kabale University |
| issn | 2095-283X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | China Science Publishing & Media Ltd. (CSPM) |
| record_format | Article |
| series | Leida xuebao |
| spelling | doaj-art-37f9e2d7bc62444197d584df6451f27c2025-01-22T06:12:25ZengChina Science Publishing & Media Ltd. (CSPM)Leida xuebao2095-283X2025-02-0114111713410.12000/JR24115R24115Noncontact Multiperson Respiratory Detection Method Based on Blind Source SeparationXuan YANG0Ziying WANG1Li ZHANG2Heng ZHAO3Hong HONG4Nanjing University of Science and Technology, Nanjing 210000, ChinaNanjing University of Science and Technology, Nanjing 210000, ChinaShanghai Aerospace Electronic Technology Institute, Shanghai 201109, ChinaNanjing University of Science and Technology, Nanjing 210000, ChinaNanjing University of Science and Technology, Nanjing 210000, ChinaIn recent years, there has been an increasing interest in respiratory monitoring in multiperson environments and simultaneous monitoring of the health status of multiple people. Among the algorithms developed for multiperson respiratory detection, blind source separation algorithms have attracted the attention of researchers because they do not require prior information and are less dependent on hardware performance. However, in the context of multiperson respiratory monitoring, the current blind source separation algorithm usually separates phase signals as the source signal. This article compares the distance dimension and phase signals under Frequency-modulated continuous-wave radar, calculates the approximate error associated with using the phase signal as the source signal, and verifies the separation effect through simulations. The distance dimension signal is better to use as the source signal. In addition, this article proposes a multiperson respiratory signal separation algorithm based on noncircular complex independent component analysis and analyzes the impact of different respiratory signal parameters on the separation effect. Simulation and experimental measurements show that the proposed method is suitable for detecting multiperson respiratory signals under controlled conditions and can accurately separate respiratory signals when the angle of the two targets to the radar is 9.46°.https://radars.ac.cn/cn/article/doi/10.12000/JR24115noncontact respiration detectionfmcw radarmultiperson respiration detectionblind source separation (bss)complex independent component analysis |
| spellingShingle | Xuan YANG Ziying WANG Li ZHANG Heng ZHAO Hong HONG Noncontact Multiperson Respiratory Detection Method Based on Blind Source Separation Leida xuebao noncontact respiration detection fmcw radar multiperson respiration detection blind source separation (bss) complex independent component analysis |
| title | Noncontact Multiperson Respiratory Detection Method Based on Blind Source Separation |
| title_full | Noncontact Multiperson Respiratory Detection Method Based on Blind Source Separation |
| title_fullStr | Noncontact Multiperson Respiratory Detection Method Based on Blind Source Separation |
| title_full_unstemmed | Noncontact Multiperson Respiratory Detection Method Based on Blind Source Separation |
| title_short | Noncontact Multiperson Respiratory Detection Method Based on Blind Source Separation |
| title_sort | noncontact multiperson respiratory detection method based on blind source separation |
| topic | noncontact respiration detection fmcw radar multiperson respiration detection blind source separation (bss) complex independent component analysis |
| url | https://radars.ac.cn/cn/article/doi/10.12000/JR24115 |
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